Feature Extraction Using Gabor Wavelets for Biometric Recognition

This document discusses the significance of MATLAB in image processing and demonstrates how to extract features using Gabor wavelets for applications in palmprint, face, and fingerprint recognition. MATLAB provides a powerful platform for analyzing and processing various types of image data through its comprehensive toolbox functions. The Gabor wavelet transformation is implemented by creating a bank of filters at multiple orientations and scales, typically using MATLAB's gaborFilterBank function. This approach captures localized frequency information and orientation characteristics from images, which are crucial for biometric feature extraction. The extracted features often include magnitude responses that can be further processed using dimensionality reduction techniques like PCA (Principal Component Analysis). For classification, we employ Support Vector Machines (SVMs) through MATLAB's fitcsvm function or libsvm library integration. The SVM classifier is trained on the extracted Gabor features using appropriate kernel functions (linear, RBF, or polynomial) to achieve optimal separation between different biometric classes. The implementation typically involves feature normalization, cross-validation for parameter tuning, and performance evaluation using metrics like accuracy and ROC curves. When applied to palmprint, face, and fingerprint recognition systems, this methodology enables the development of robust authentication systems. These technologies have widespread applications in security systems and personal identity verification, ensuring data protection and restricted access to authorized users only. The code implementation typically follows a pipeline: image preprocessing -> Gabor feature extraction -> feature selection -> SVM training/testing -> performance evaluation.